Publication Details

Category Text Publication
Reference Category Journals
DOI 10.1109/JSTARS.2022.3166974
Licence creative commons licence
Title (Primary) Estimating high-resolution soil moisture over mountainous regions using remotely-sensed multispectral and topographic data
Author Fan, L.; Al-Yaari, A.; Frappart, F.; Peng, J. ORCID logo ; Wen, J.; Xiao, Q.; Jin, R.; Li, X.; Liu, X.; Wang, M.; Chen, X.; Zhao, L.; Ma, M.; Wigneron, J.-P.
Source Titel IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Year 2022
Department RS
Volume 15
Page From 3637
Page To 3649
Language englisch
Topic T5 Future Landscapes
Keywords Indexes; Vegetation mapping; Radio frequency; Optical sensors; Microwave measurement; Rivers; Optical reflection; High resolution; mountainous regions; optical index; random forest (RF) method; soil moisture (SM); thermal index
Abstract A surface soil moisture (SM) condition at high spatiotemportal resolutions is required by regional Earth system applications. Here, we mapped daily 1-km SM in the Babao River Basin in the northwest of China during the summers from 2013 to 2015 using a random forest (RF) method by merging SM information retrieved from in situ measurements, optical/thermal remote sensing, and topographical indices. Relative importance analysis was used to determine the optimal predictors for estimating high-resolution SM. A specific RF model (RFVI+sup) was constructed using the optimal predictors including remote sensing albedo, apparent thermal inertia (ATI), normalized difference vegetation index, normalized difference infrared index 5, soil adjusted vegetation index, and topographical indices (aspect and elevation). The RFVI+sup also accounted for missing observations of the thermal index (e.g., ATI) over the mountainous regions. In the comparison between the SM estimates using the new RFVI+sup model and other RF models, the spatial coverage of available estimates increased from 14% to 64% over the study region, the correlation coefficient values were improved to 0.75, the unbiased root-mean-squared difference values decreased to 0.032 m3/m3. Thus, the proposed RF method provided accurate SM estimates with high spatiotemporal resolution over the mountainous regions, by merging multiresource datasets from in situ measurements, remotely-sensed, and topographical indices.
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Fan, L., Al-Yaari, A., Frappart, F., Peng, J., Wen, J., Xiao, Q., Jin, R., Li, X., Liu, X., Wang, M., Chen, X., Zhao, L., Ma, M., Wigneron, J.-P. (2022):
Estimating high-resolution soil moisture over mountainous regions using remotely-sensed multispectral and topographic data
IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens. 15 , 3637 - 3649 10.1109/JSTARS.2022.3166974